'''simple docstring'''




import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Tuple:
    # Initialise PyTorch model
    __lowerCamelCase            =    LxmertConfig.from_json_file(UpperCamelCase__							)
    print(f"""Building PyTorch model from configuration: {config}"""							)
    __lowerCamelCase            =    LxmertForPreTraining(UpperCamelCase__							)

    # Load weights from tf checkpoint
    load_tf_weights_in_lxmert(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)

    # Save pytorch-model
    print(f"""Save PyTorch model to {pytorch_dump_path}"""							)
    torch.save(model.state_dict()							,    UpperCamelCase__							)


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  __UpperCAmelCase					=parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       float:
    return base * power(UpperCamelCase__							,    (exponent - 1)							) if exponent else 1


if __name__ == "__main__":
  print("Raise base to the power of exponent using recursion...")
  __UpperCAmelCase					=int(input("Enter the base: ").strip())
  __UpperCAmelCase					=int(input("Enter the exponent: ").strip())
  __UpperCAmelCase					=power(base, abs(exponent))
  if exponent < 0:  # power() does not properly deal w/ negative exponents
    __UpperCAmelCase					=1 / result
  print(f'{base} to the power of {exponent} is {result}')
